Multi-scale directional-filtering-based method for follicular lymphoma grading

Follicular lymphoma (FL) is a group of malignancies of lymphocyte origin that arise from lymph nodes, spleen, and bone marrow in the lymphatic system. It is the second most common non-Hodgkins lymphoma. Characteristic of FL is the presence of follicle center B cells consisting of centrocytes and centroblasts. Typically, FL images are graded by an expert manually counting the centroblasts in an image. This is time consuming. In this paper, we present a novel multi-scale directional filtering scheme and utilize it to classify FL images into different grades. Instead of counting the centroblasts individually, we classify the texture formed by centroblasts. We apply our multi-scale directional filtering scheme in two scales and along eight orientations, and use the mean and the standard deviation of each filter output as feature parameters. For classification, we use support vector machines with the radial basis function kernel. We map the features into two dimensions using linear discriminant analysis prior to classification. Experimental results are presented.     

Optimum mesh grading for finite-difference method

The coarseness error of the finite-difference (FD) method is studied analyzing a typical planar waveguide and a rectangular coaxial geometry. Results for equidistant and graded mesh are compared in terms of accuracy and numerical efforts. Because of the field singularities involved a graded mesh proves to be superior compared to the equidistant case. A grading strategy with optimum efficiency is presented. Furthermore, the results show that the most significant improvement in accuracy can be obtained by incorporating the edge behavior into the FD scheme     

机载激光雷达数据中道路中线的多尺度提取方法

机载激光雷达(LIDAR)技术的出现为道路特征的获取提供了新的途径.在分析现有道路提取现状的基础上,针对激光雷达数据的特点以及单一尺度下道路中线提取方法的不足,提出一种基于多尺度追踪的道路中线提取方法.该方法首先采用逐步约束的方法进行道路激光点的提取,包括高程约束、强度约束以及区域点密度和区域面积的约束等;然后基于道路点云生成的不同尺度距离影像的形态学细化结果,采用多尺度追踪的方法实现道路中线的提取,其中多尺度追踪方法由大尺度道路中线的迭代追踪以及小尺度道路中线的启发式追踪两部分组成.最后采用实地数据进行验证,结果表明:该方法能有效地从LIDAR点云中提取道路中线信息,并具有较好的精度.     

A novel method for the automatic grading of retinal vessel tortuosity

Tortuosity is among the first alterations in the retinal vessel network to appear in many retinopathies, such as those due to hypertension. An automatic evaluation of retinal vessel tortuosity would help the early detection of such retinopathies. Quite a few techniques for tortuosity measurement and classification have been proposed, but they do not always match the clinical concept of tortuosity. This justifies the need for a new definition, able to express in mathematical terms the tortuosity as perceived by ophthalmologists. We propose here a new algorithm for the evaluation of tortuosity in vessels recognized in digital fundus images. It is based on partitioning each vessel in segments of constant-sign curvature and then combining together each evaluation of such segments and their number. The algorithm has been compared with other available tortuosity measures on a set of 30 arteries and one of 30 veins from 60 different images. These vessels had been preliminarily ordered by a retina specialist by increasing perceived tortuosity. The proposed algorithm proved to be the best one in matching the clinically perceived vessel tortuosity.     

A subject-independent method for automatically grading electromyographic features during a fatiguing contraction

Many studies have attempted to monitor fatigue from electromyogram (EMG) signals. However, fatigue affects EMG in a subject-specific manner. We present here a subject-independent framework for monitoring the changes in EMG features that accompany muscle fatigue based on principal component analysis and factor analysis. The proposed framework is based on several time- and frequency-domain features, unlike most of the existing work, which is based on two to three features. Results show that latent factors obtained from factor analysis on these features provide a robust and unified framework. This framework learns a model from EMG signals of multiple subjects, that form a reference group, and monitors the changes in EMG features during a sustained submaximal contraction on a test subject on a scale from zero to one. The framework was tested on EMG signals collected from 12 muscles of eight healthy subjects. The distribution of factor scores of the test subject, when mapped onto the framework was similar for both the subject-specific and subject-independent cases.     

基于EMD-LSSVM的多尺度混合建模方法及其应用

激光陀螺漂移时间序列具有非平稳和非线性的特点,单一预测模型难以准确跟踪其变化趋势。研究了基于经验模态分解（EMD）和最小二乘支持向量机（LSSVM）的多尺度混合建模方法及在激光陀螺漂移预测中的应用。首先,利用经验模态分解将漂移时间序列分解为多个本征模式分量,在采用具有适当核函数的最小二乘支持向量机分别对这些分量进行预测后,以加权集成方式得到最终预测结果。最后,将该方法用于激光陀螺的随机漂移预测中,仿真结果表明:该方法能够准确预测激光陀螺漂移值,取得了比单一模型更好的预测效果,能够为激光陀螺的漂移补偿、故障预报和可靠性诊断提供参考。     

Using adaptive neuro-fuzzy inference systems for the detection of centroblasts in microscopic images of follicular lymphoma

In this paper, a complete methodology for automatic detection of centroblasts (CBs) in microscopic images acquired from tissue biopsies of follicular lymphoma is presented. In the proposed method, tissue sections are sliced at a low thickness level, around 1–1.5 \(\upmu \)m, which provides a more detailed depiction of the nuclei and other textural information. Initially, images are segmented into their basic cytological components, i.e., blood cells, nuclei and extra-cellular material, and then a novel touching-cell splitting algorithm is applied using a Gaussian mixture model and expectation–maximization algorithm. Additionally, a morphological and textural analysis of CBs is applied in order to extract various features related to their nuclei, nucleoli and cytoplasm. In the final step, a novel classification scheme is proposed based on adaptive neuro-fuzzy inference systems to classify the candidate cells. The methodology yielded promising results with an average detection rate of 90.35 %.     

基于深度学习的钢材金相图像晶粒度等级评定方法

准确地评定钢材金相组织晶粒度等级能检测材料劣化情况,保障设备的服役安全。针对传统人工评定钢材金相组织晶粒度等级的方法耗时久且易受人工经验影响,评价结果一致性差且不可重复等问题,提出了一种基于深度学习的钢材金相晶粒度等级评定方法。在U-net模型上添加跳跃连接层并减少下采样次数来提高模型的分割准确率并减少网络参数量,在117张验证集上的像素准确率达93.86%,平均像素准确率(mean pixel accuracy,MPA)达86.89%,网络参数量仅为2.02 M。对晶界预测结果进行数字图像处理并结合截点法进行晶粒度等级评定,在测试图像上评定钢材晶粒度等级平均耗时仅8.3 s/张。与人工评级方法相比,本文方法具有准确性、高效性及可重复性。